{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 泰坦尼克号预测分析2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.数据总览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   Parch     Ticket     Fare Cabin Embarked  \n",
       "0      0  A/5 21171   7.2500   NaN        S  \n",
       "1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('titanic_train.csv')\n",
    "test_data = pd.read_csv('titanic_test.csv')\n",
    "# sns.set_style('whitegrid')\n",
    "train_data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "----------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "Survived       418 non-null int64\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()\n",
    "print('-' * 40)\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制存活的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd25633278>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Survived'].value_counts().plot.pie(labeldistance=1.1,     # 标签与图的距离\n",
    "                                               autopct='%1.2f%%',     # 圆里的文本格式\n",
    "                                               shadow=False,          # 饼是否有阴影\n",
    "                                               startangle=90,         # 起始角度，0，表示从0开始逆时针转，一般选择90\n",
    "                                               pctdistance=0.6)       # 饼状图返回值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.缺失数据处理：\n",
    "* 1.如果数据集很多，缺失值很少，可以删除带缺失值的行\n",
    "* 2.如果该属性相对机器学习来说不是很重要，可以对缺失值赋均值或者众数\n",
    "* 3.对于标称属性，缺失本身也可能代表着一些属性\n",
    "* 4.使用随机森林等模型预测缺失属性的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor \n",
    "\n",
    "age_df = train_data[['Age', 'Survived', 'Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "age_df_notnull = age_df.loc[(train_data['Age'].notnull())]\n",
    "age_df_isnull = age_df.loc[(train_data['Age'].isnull())]\n",
    "X = age_df_notnull.values[:, 1:]\n",
    "Y = age_df_notnull.values[:, 0]\n",
    "\n",
    "# 用随机森林回归训练数据\n",
    "rfr = RandomForestRegressor(n_estimators=1000, n_jobs=-1)\n",
    "rfr.fit(X, Y)\n",
    "predictAges = rfr.predict(age_df_isnull.values[:, 1:])\n",
    "train_data.loc[train_data['Age'].isnull(), ['Age']] = predictAges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Cabin'] = train_data.Cabin.fillna('U0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          891 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.分析数据关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 性别与生存与否的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            81\n",
       "        1           233\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex', 'Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[['Sex', 'Survived']].groupby(['Sex']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd25a8eba8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex', 'Survived']].groupby(['Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2船舱等级和生存的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Survived\n",
       "1       0            80\n",
       "        1           136\n",
       "2       0            97\n",
       "        1            87\n",
       "3       0           372\n",
       "        1           119\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Pclass', 'Survived'])['Pclass'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[['Pclass', 'Survived']].groupby(['Pclass']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd27ade048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Pclass', 'Survived']].groupby(['Pclass']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3不同等级的船舱男女生存率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd27b4c400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex', 'Pclass', 'Survived']].groupby(['Pclass', 'Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass  Survived\n",
       "female  1       0             3\n",
       "                1            91\n",
       "        2       0             6\n",
       "                1            70\n",
       "        3       0            72\n",
       "                1            72\n",
       "male    1       0            77\n",
       "                1            45\n",
       "        2       0            91\n",
       "                1            17\n",
       "        3       0           300\n",
       "                1            47\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex', 'Pclass', 'Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 年龄与存活的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Sex and Age vs Survived')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(10,5))\n",
    "ax[0].set_yticks(range(0,110,10))\n",
    "sns.violinplot('Pclass', 'Age', hue='Survived', data=train_data, split=True, ax=ax[0])\n",
    "ax[0].set_title('Pclass and Age vs Survived')\n",
    "\n",
    "ax[1].set_yticks(range(0, 110, 10))\n",
    "sns.violinplot('Sex', 'Age', hue='Survived', data=train_data, split=True, ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 总体年龄分布\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(121)\n",
    "train_data['Age'].hist(bins=20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Num')\n",
    "\n",
    "plt.subplot(122)\n",
    "train_data.boxplot(column='Age', showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bd27cba630>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 915.875x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同年龄下的生存和非生存的分布情况\n",
    "facet = sns.FacetGrid(train_data, hue='Survived', aspect=4)\n",
    "facet.map(sns.kdeplot, 'Age', shade=True)\n",
    "facet.set(xlim=(0, train_data['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd27e039b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同年龄下的平均生存率\n",
    "fig, axis1 = plt.subplots(1,1, figsize=(18,5))\n",
    "train_data['Age_int'] = train_data['Age'].astype(int)\n",
    "average_age = train_data[['Age_int', 'Survived']].groupby(['Age_int'], as_index=False).mean()\n",
    "sns.barplot(x='Age_int', y='Survived', data=average_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.662418\n",
       "std       13.738370\n",
       "min        0.420000\n",
       "25%       21.000000\n",
       "50%       28.000000\n",
       "75%       37.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age_group\n",
       "(0, 12]      0.506173\n",
       "(12, 18]     0.466667\n",
       "(18, 65]     0.364512\n",
       "(65, 100]    0.125000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 12, 18, 65, 100]\n",
    "train_data['Age_group'] = pd.cut(train_data['Age'], bins)\n",
    "by_age = train_data.groupby('Age_group')['Survived'].mean()\n",
    "by_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 称呼与存活的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Title'] = train_data['Name'].str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "pd.crosstab(train_data['Title'], train_data['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd27d2f3c8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Title', 'Survived']].groupby(['Title']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd282c4550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 名字产犊和存活的关系\n",
    "fig, axis1 = plt.subplots(1,1, figsize=(18,5))\n",
    "train_data['Name_length'] = train_data['Name'].apply(len)\n",
    "name_length = train_data[['Name_length', 'Survived']].groupby(['Name_length'], as_index=False).mean()\n",
    "sns.barplot(x='Name_length', y='Survived', data=name_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 有无兄弟姐妹和存活的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd28828128>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sibsp_df = train_data[train_data['SibSp'] !=0]\n",
    "no_sibsp_df = train_data[train_data['SibSp'] ==0]\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(121)\n",
    "sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct='%1.2f%%')\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(122)\n",
    "no_sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived', 'Survived'], autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7亲友人数和存活的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SibSpa adn Survived')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
    "train_data[['Parch', 'Survived']].groupby(['Parch']).mean().plot.bar(ax=ax[0])\n",
    "ax[0].set_title('Parch and Survived')\n",
    "\n",
    "train_data[['SibSp', 'Survived']].groupby(['SibSp']).mean().plot.bar(ax=ax[1])\n",
    "ax[1].set_title('SibSpa adn Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.8 票价分布和生存的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AwABCFgDAAEIWAMAAQhYAwABCFgDAAEIWAMAAQhYAwACu+M6WN+rK9edy9XoAlhOy2FDrFYAAYL05XAgAMICQBQAwwJDDhVV1c5L/mOSiJF9urd0zYj/A2nMOG8DaWPOQVVUXJfnPSf55khNJvl1Vj7TW/mSt9wXwdtYjMAqLwFsZMZN1XZJjrbUXk6SqfifJbUmELADYps73Q8+Ba87ks+/gd6f0wWdEyNqZ5KVl6yeS/NMB+4FJMWvyzvhmKbDVVWttbZ+w6o4k/6K19q/6+s8mua619m/O6bc/yf6++o+T/NmaDuTNrkjyF4P3wXzUYjrUYjrUYjrUYlqmWI9/2Fr7wGqdRsxknUhy9bL1XUlOntuptXYoyaEB+19RVT3dWtu7XvvjranFdKjFdKjFdKjFtGzmeoy4hMO3k+ypqg9W1buTfDrJIwP2AwAwWWs+k9VaO1NV/zrJf8vSJRy+0lr73lrvBwBgyoZcJ6u19miSR0c89wVYt0OTrEotpkMtpkMtpkMtpmXT1mPNT3wHAMBtdQAAhhCyAAAG2BYhq6purqo/q6pjVXVwo8ez1VXVV6rqVFV9d1nbZVX1WFW90B/f39urqr7Ya/NcVX1840a+9VTV1VX1ZFU9X1Xfq6rP93b1WGdV9d6q+sOq+uNei1/p7R+sqqd6Lb7Wv5WdqnpPXz/Wt+/eyPFvRVV1UVV9p6q+0dfVYgNU1fGqOlpVz1bV071tS/yN2vIha9m9FD+R5CNJfqaqPrKxo9ryfiPJzee0HUzyeGttT5LH+3qyVJc9/Wd/ki+t0xi3izNJDrTWPpzk+iR393//6rH+fpTkxtbaR5Ncm+Tmqro+yReS3Ndr8VqSu3r/u5K81lr7UJL7ej/W1ueTPL9sXS02zk+01q5ddj2sLfE3asuHrCy7l2Jr7f8kOXsvRQZprf1BklfPab4tyeG+fDjJ7cvav9qWfCvJpVV11fqMdOtrrb3cWvujvvxXWfoPZWfUY9319/R0X31X/2lJbkzy9d5+bi3O1ujrSW6qqlqn4W55VbUrya1JvtzXK2oxJVvib9R2CFkr3Utx5waNZTtbaK29nCz9x5/kyt6uPuukH+L4WJKnoh4boh+eejbJqSSPJfnzJK+31s70Lsvf77+rRd/+RpLL13fEW9qvJ/mFJH/b1y+PWmyUluT3q+qZfsu9ZIv8jRpynayJWenThutWTIf6rIOq+rEkv5vk51prf/k2H8LVY6DW2t8kubaqLk3ycJIPr9StP6rFIFX1ySSnWmvPVNXi2eYVuqrF+rihtXayqq5M8lhV/enb9N1UtdgOM1lz3UuR4V45O6XbH0/1dvUZrKrelaWA9WBr7fd6s3psoNba60lmWTpP7tKqOvuBd/n7/Xe16Nvflzcfhuf83JDkp6vqeJZOIbkxSzNbarEBWmsn++OpLH34uC5b5G/UdghZ7qU4DY8k2deX9yU5sqz9M/0bI9cneePsFDEXrp838kCS51trv7Zsk3qss6r6QJ/BSlVdnOQns3SO3JNJPtW7nVuLszX6VJInmqtHr4nW2i+21na11nZn6f+EJ1prd0Yt1l1VXVJVP352OclPJflutsjfqG1xxfequiVLn1LO3kvxVzd4SFtaVf12ksUkVyR5JckvJ/kvSR5K8g+SfD/JHa21V3sI+E9Z+jbiD5N8rrX29EaMeyuqqn+W5L8nOZr/d+7JL2XpvCz1WEdV9U+ydALvRVn6gPtQa+3fV9U/ytJsymVJvpPkX7bWflRV703ym1k6j+7VJJ9urb24MaPfuvrhwn/bWvukWqy//p4/3Fd3JPmt1tqvVtXl2QJ/o7ZFyAIAWG/b4XAhAMC6E7IAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAG+L9M8qIp2jpODgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "train_data['Fare'].hist(bins=20)\n",
    "train_data.boxplot(column='Fare', by='Pclass')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.特征缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.558052\n",
       "1    0.607224\n",
       "2   -0.266733\n",
       "3    0.388735\n",
       "4    0.388735\n",
       "Name: Age_scaled, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing \n",
    "\n",
    "# StandarScaler 将每个值减去平均值，然后再放缩到数据的方差\n",
    "scaler = preprocessing.StandardScaler()\n",
    "train_data['Age_scaled'] = scaler.fit_transform(train_data['Age'].values.reshape(-1,1))\n",
    "train_data['Age_scaled'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  4.1 将连续数值离散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (-0.001, 7.854]\n",
       "1    (39.688, 512.329]\n",
       "2        (7.854, 10.5]\n",
       "Name: Fare_bin, dtype: category\n",
       "Categories (5, interval[float64]): [(-0.001, 7.854] < (7.854, 10.5] < (10.5, 21.679] < (21.679, 39.688] < (39.688, 512.329]]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Fare_bin'] = pd.qcut(train_data['Fare'], 5)\n",
    "train_data['Fare_bin'][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 特征工程\n",
    "需要将训练数据和测试数据一起处理，使得具有相同的数据烈性和数据分布 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 特征间相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bd287ff7f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = pd.DataFrame(train_data[['Name_length','Fare','Pclass','Age']])\n",
    "colormap = plt.cm.viridis\n",
    "plt.title('Pearson correaltion of feature')\n",
    "sns.heatmap(correlation.astype(float).corr(), linewidths=0.1, vmax=1.0, square=True,\n",
    "            cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.11 特征之间的数据分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-32-7d8b58f678c8>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-32-7d8b58f678c8>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    hue='Survived'b)\u001b[0m\n\u001b[1;37m                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "g = sns.pairplot(train_data[['Survived', 'Name_length','Fare','Pclass','Age']],\n",
    "                hue='Survived'b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 模型测试及融合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 利用不同的模型对特征进行筛选，选出较为重要的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 判断特征的重要性\n",
    "from sklearn.feature_selection import SelectKBest, f_classif \n",
    "\n",
    "predictors = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
    "selector = SelectKBest(f_classif, k=5)\n",
    "selector.fit(train_data[predictors], train_data['Survived'])\n",
    "scores = -np.log(selector.pvalues_)\n",
    "\n",
    "plt.bar(range(len(predictors)), scores)\n",
    "plt.xticks(range(len(predictors)), predictors, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机森林选出top n 特征\n",
    "from sklearn.ensemble import RandomForestCLassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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